Optimized Dynamic Monitoring and Quality Management System for Post-Harvest Matsutake of Different Preservation Packaging in Cold Chain

Exploring the therapeutic potential of the oxygenated monoterpene linalool in alleviating saline stress effects on Allium cepa L

Sodium chloride (NaCl) can cause oxidative stress in plants, which represents a potential obstacle to the development of monocultures worldwide. Onion (Allium cepa L.) is a famous vegetable consumed and used in world cuisine. In the present study, we analyzed the influence of soil physicochemical profile and the remedial capacity of linalool on seed emergence, roots, and leaf growth in onions subjected to salt stress, as well as its in vivo and in vitro antioxidant potential, Fe2+chelating activity, and reducing power of Fe3+. The outcome of the soil analysis established the following order of abundance: sulfur (S) > calcium (Ca) > potassium (K) > magnesium (Mg) > sodium (Na). NaCl (150 mM) significantly reduced the emergence speed index (ESI), leaf and root length, while increasing the peroxidation content. The length of leaves and roots significantly increased after treatment with linalool (300 and 500 μg/mL). Our data showed negative correlations between seed emergence and K+ concentration, which was reversed after treatments. Linalool (500 μg/mL) significantly reduced oxidative stress, but increased Fe2+ concentration and did not show potential to reduce Fe3+. The in vivo antioxidant effect of linalool is thought to primarily result from an enzymatic activation process. This mechanism underscores its potential as a therapeutic agent for oxidative stress-related conditions. Further investigation into this process could unveil new avenues for antioxidant therapy.

Polyphenolic profile, processing impact, and bioaccessibility of apple fermented products

Health-promoting foods have become increasingly popular due to intensified consumer interest and awareness of illnesses. There is a global market for apple fruits, which are affordable, nutritious, tasty, and produced in large quantities for direct consumption as well as food processing to make derived products. The food matrix of apples is suitable for fermentation, besides containing a high amount of phenolics and polyphenols. Fermentation of apples is one of the most common methods of preserving apple fruit and its byproducts. With different fermentation techniques, apple fruit can be used to make a wide range of products, such as fermented apple juice, cider, liqueurs, apple cider, apple vinegar and fermented apple solids, because it is not only a low-cost and simple method of processing the fruit, but it can also sometimes increase the bioavailability of nutrients and the levels of components that can improve health and sensory quality. To understand the health benefits of food products and how the fermentation process impacts polyphenols, it is also crucial to observe the effects of digestion on polyphenol bioaccessibility. Polyphenolic profile changes can be observed via both in vitro and in vivo digestion methods; however, in vitro digestion methods have the advantage of observing every step of gastrointestinal track effects and have less cost as well. In this review, the polyphenolic profile, processing impact, and bioaccessibility of apple-fermented products is assessed, with most available studies showing polyphenol profiles and bioaccessibility in apple varieties and fermented apple products.

Revolutionizing single-use food packaging: a comprehensive review of heat-sealable, water-soluble, and edible pouches, sachets, bags, or packets

Edible food packaging has emerged as a critical focal point in the discourse on sustainability, prompting the development of innovative solutions, notably in the realm of edible pouches. Often denoted as sachets, bags, or packets, these distinct designs have garnered attention owing to their water-soluble and heat-sealable attributes, tailored explicitly for single-use applications encompassing oils, instant or dry foods, and analogous products. While extant literature extensively addresses diverse facets of edible films, this review addresses a conspicuous void by presenting a consolidated and specialized overview dedicated to the intricate domain of edible pouches. Through a meticulous synthesis of current research, we aim to illuminate the trajectory of advancements made thus far, delving into critical aspects, including materials, production techniques, functional attributes, consumer perceptions, and regulatory considerations. By furnishing a comprehensive perspective on the potential, challenges, and opportunities inherent in edible pouches, our overarching aim is to stimulate collaborative endeavors in research, innovation, and exploration. In doing so, we aspire to catalyze the broader adoption of sustainable packaging solutions tailored to the exigencies of single-use applications.

Simultaneous Quantitative Determination of Low-Concentration Preservatives and Heavy Metals in Tricholoma Matsutakes Based on SERS and FLU Spectral Data Fusion

As an ingredient of great economic value, Tricholoma matsutake has received widespread attention. However, heavy metal residues and preservatives in it will affect the quality of Tricholoma matsutake and endanger the health of consumers. Here, we present a method for the simultaneous detection of low concentrations of potassium sorbate and lead in Tricholoma matsutakes based on surface-enhanced Raman spectroscopy (SERS) and fluorescence (FLU) spectroscopy to test the safety of consumption. Data fusion strategies combined with multiple machine learning methods, including partial least-squares regression (PLSR), deep forest (DF) and convolutional neural networks (CNN) are used for model training. The results show that combined with reasonable band selection, the CNN prediction model based on decision-level fusion achieves the best performance, the correlation coefficients (R2) were increased to 0.9963 and 0.9934, and the root mean square errors (RMSE) were reduced to 0.0712 g·kg-1 and 0.0795 mg·kg-1, respectively. The method proposed in this paper accurately predicts preservatives and heavy metals remaining in Tricholoma matsutake and provides a reference for other food safety testing.

The concentration of pesticides in tomato: a global systematic review, meta-analysis, and health risk assessment

To improve farming productivity, a large number of pesticides have been used worldwide in recent decades, leading to the pollution of soil, agri-products, and water, directly/indirectly affecting human health. In this regard, many studies were conducted in different countries on residual pesticides in the environment. In the current study, residual pesticides including chlorpyrifos, cypermethrin, diazinon, malathion, and metalaxyl in tomatoes were meta-analyzed and health risk of consumers was estimated. For this purpose, based on a systematic review, data from 47 studies were extracted and meta-analyzed, and the health impact of pooled concentrations was assessed via a health risk method. According to the results, metalaxyl had the most concentration followed by malathion, cypermethrin, diazinon, and chlorpyrifos, respectively. The non-carcinogenic risk (n-CR) was calculated from crop consumption also showed that exposure to malathion has the most risk. Among the investigated communities, Iranian consumers were in considerable health risk (THQ > 1). Considering that the potential for the use of pesticides will increase with the need for food in the future, hence, governments must manage the usage by governments via alternative methods such as cultural, biological, physical, and genetic modifications.

A fuzzy mathematical model for hybrid inventory and purchase optimization in a reverse logistics system considering shortage and warehouse capacity

Making decisions about the design and implementation of a logistics network is crucial as it has long-term impacts. However, it is important to consider that demand factors and the number of returned items by customers may change over time. Therefore, it is necessary to design a logistics network that can adapt to various demand fluctuations. The main goal of this study is to calculate the quantity of products that should be sent at different times in a supply chain network to minimize the overall cost of reverse logistics and tardiness time. Accordingly, a multi-objective mathematical model is proposed that aims to optimize the total cost and the amount of delay in sending customer orders in a three-level logistics network, assuming that some parameters are uncertain. Additionally, the minimization of waiting time, considering the level of delay in sending, is applied as the second objective function. To handle the uncertainty in the reverse logistics network, a fuzzy approach is implemented, and the proposed model is solved using GAMS software. Furthermore, to solve the mathematical model in large dimensions, the Cuckoo Optimization Algorithm (COA) is applied in MATLAB software, and the results are compared to the global optimal solution. The outcomes show that the proposed algorithm has a desirable performance, as the total values sent to the manufacturer are equal to those obtained from the exact solution, and the objective function value decreases as the number of repetitions increases.

The food and biomedical applications of curcumin-loaded electrospun nanofibers: A comprehensive review

Encapsulating curcumin (CUR) in nanocarriers such as liposomes, polymeric micelles, silica nanoparticles, protein-based nanocarriers, solid lipid nanoparticles, and nanocrystals could be efficient for a variety of industrial and biomedical applications. Nanofibers containing CUR represent a stable polymer-drug carrier with excellent surface-to-volume ratios for loading and cell interactions, tailored porosity for controlled CUR release, and diverse properties that fit the requirements for numerous applications. Despite the mentioned benefits, electrospinning is not capable of producing fibers from multiple polymers and biopolymers, and the product's effectiveness might be affected by various machine- and material-dependent parameters like the voltage and the flow rate of the electrospinning process. This review delves into the current and innovative recent research on nanofibers containing CUR and their various applications.

Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments

Matsutake mushrooms, known for their high value, present challenges due to their seasonal availability, difficulties in harvesting, and short shelf life, making it crucial to extend their post-harvest preservation period. In this study, we developed three quality predictive models of Matsutake mushrooms using three different methods. The quality changes of Matsutake mushrooms were experimentally analyzed under two cases (case A: Temperature control and sealing measures; case B: Alteration of gas composition) with various parameters including the hardness, color, odor, pH, soluble solids content (SSC), and moisture content (MC) collected as indicators of quality changes throughout the storage period. Prediction models for Matsutake mushroom quality were developed using three different methods based on the collected data: multiple linear regression (MLR), support vector regression (SVR), and an artificial neural network (ANN). The comparative results reveal that the ANN outperforms MLR and SVR as the optimal model for predicting Matsutake mushroom quality indicators. To further enhance the ANN model's performance, optimization techniques such as the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm techniques were employed. The optimized ANN model achieved impressive results, with an R-Square value of 0.988 and an MSE of 0.099 under case A, and an R-Square of 0.981 and an MSE of 0.164 under case B. These findings provide valuable insights for the development of new preservation methods, contributing to the assurance of a high-quality supply of Matsutake mushrooms in the market.

Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.